scholarly journals Consistency of the maximum likelihood and variational estimators in a dynamic stochastic block model

2019 ◽  
Vol 13 (2) ◽  
pp. 4157-4223
Author(s):  
Léa Longepierre ◽  
Catherine Matias
Keyword(s):  
Maximum Likelihood ◽  
Block Model ◽  
Stochastic Block Model ◽  
Dynamic Stochastic

scholarly journals Consistency of maximum-likelihood and variational estimators in the stochastic block model

2012 ◽  
Vol 6 (0) ◽  
pp. 1847-1899 ◽  
Author(s):  
Alain Celisse ◽  
Jean-Jacques Daudin ◽  
Laurent Pierre
Keyword(s):  
Maximum Likelihood ◽  
Block Model ◽  
Stochastic Block Model

scholarly journals Exact Recovery of Stochastic Block Model by Ising Model

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 65
Author(s):  
Feng Zhao ◽  
Min Ye ◽  
Shao-Lun Huang
Keyword(s):  
Phase Transition ◽  
Maximum Likelihood ◽  
Ising Model ◽  
Optimization Problem ◽  
Model Parameters ◽  
Stochastic Algorithm ◽  
Block Model ◽  
Stochastic Block Model ◽  
Special Case ◽  
Transition Property

In this paper, we study the phase transition property of an Ising model defined on a special random graph—the stochastic block model (SBM). Based on the Ising model, we propose a stochastic estimator to achieve the exact recovery for the SBM. The stochastic algorithm can be transformed into an optimization problem, which includes the special case of maximum likelihood and maximum modularity. Additionally, we give an unbiased convergent estimator for the model parameters of the SBM, which can be computed in constant time. Finally, we use metropolis sampling to realize the stochastic estimator and verify the phase transition phenomenon thfough experiments.

scholarly journals Weighted stochastic block model

2021 ◽  
Author(s):  
Tin Lok James Ng ◽  
Thomas Brendan Murphy
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Simulated Data ◽  
Important Case ◽  
Block Model ◽  
Data Set ◽  
Stochastic Block Model ◽  
Proposed Model ◽  
Variational Approaches ◽  
Number Of Classes

AbstractWe propose a weighted stochastic block model (WSBM) which extends the stochastic block model to the important case in which edges are weighted. We address the parameter estimation of the WSBM by use of maximum likelihood and variational approaches, and establish the consistency of these estimators. The problem of choosing the number of classes in a WSBM is addressed. The proposed model is applied to simulated data and an illustrative data set.

Fast Algorithm on Stochastic Block Model for Exploring General Communities

2014 ◽  
Vol 24 (11) ◽  
pp. 2699-2709 ◽  
Author(s):  
Bian-Fang CHAI ◽  
Jian YU ◽  
Cai-Yan JIA ◽  
Jing-Hong WANG
Keyword(s):  
Fast Algorithm ◽  
Block Model ◽  
Stochastic Block Model
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